Most of the works on this topic which aim to give a general expression for the gradient of information measures, such as [1]} and [2]}, are based on the statistical representation of the input-output relation of the system by use of probabilistic descriptor. However, we take a different approach by returning to the idea first described by Wiener that, input-output relation of a stochastic system can be described in an alternative manner by using a functional approach [3]}. Taking this functional approach enables us to give a general expression for the gradient of mutual information with respect to the system parameters. We model the system as a known function that relates the input and output of the system. Also, we impose no restrictions on the system input to be independent of the system parameter or even of the system noise.
